MOMI-Cosegmentation:
Simultaneous Segmentation of
Multiple Objects
among Multiple Images
Wen-Sheng Chu†, Chia-Ping Chen‡¶, Chu-Song Chen†¶
† Research Center for Information Technology Innovation, Academia Sinica, Taipei 115, Taiwan
‡ Institute of Information Science, Academia Sinica, Taipei 115, Taiwan
¶ Dept. of CSIE, National Taiwan University, Taipei 106, Taiwan
{wschu,cpchen,song}@iis.sinica.edu.tw
Introduction
-
We introduce a new cosegmentation approach, MOMI-cosegmentation, to segment multiple objects
that repeatedly appear among multiple images.
The proposed approach tackles a more general problem than conventional cosegmentation methods.
The key idea of MOMI-cosegmentation is to incorporate a common pattern discovery algorithm
with the proposed Gibbs energy model in a Markov random field framework.
Our approach builds upon an observation that the detected common patterns provide
useful information for estimating foreground statistics, while background statistics
can be estimated from the remaining pixels.
The initialization and segmentation processes of MOMI-cosegmentation are completely
automatic, while the segmentation errors can be substantially reduced at the same time.
Experimental results demonstrate the effectiveness of the proposed approach over
state-of-the-art cosegmentation method.
Download
Reference
-
MOMI-Cosegmentation: Simultaneous Segmentation of Multiple Objects
among Multiple Images
Wen-Sheng Chu, Chia-Ping Chen and Chu-Song Chen
Asian Conference on Computer Vision (ACCV) 2010, Queenstown, New Zealand.
[ pdf ]
[ presentation ]
[ supplementary (15,355KB) ]
[ bibtex ]
Copyright
-
These datasets are collected from Flickr and Google image search without
permission from the original copyright holders.
By downloading these files, you agree not to hold the authors or Academia Sinica
liable for any damage, lawsuits, or other loss resulting from the possession or use of files.
If you are the copyright owner of one of these images and would like it removed from the dataset,
please contact Wen-Sheng Chu.
Release Notes
- [Nov 12, 2010] Paper, presentation and supplementary online.
- [Sep 24, 2010] First release of dataset and groundrturh.
Feedback
- Wen-Sheng Chu (wensheng.chu[at]gmail.com)
Introduction- We introduce a new cosegmentation approach, MOMI-cosegmentation, to segment multiple objects that repeatedly appear among multiple images. The proposed approach tackles a more general problem than conventional cosegmentation methods. The key idea of MOMI-cosegmentation is to incorporate a common pattern discovery algorithm with the proposed Gibbs energy model in a Markov random field framework. Our approach builds upon an observation that the detected common patterns provide useful information for estimating foreground statistics, while background statistics can be estimated from the remaining pixels. The initialization and segmentation processes of MOMI-cosegmentation are completely automatic, while the segmentation errors can be substantially reduced at the same time. Experimental results demonstrate the effectiveness of the proposed approach over state-of-the-art cosegmentation method.
Download Reference-
MOMI-Cosegmentation: Simultaneous Segmentation of Multiple Objects
among Multiple Images
Wen-Sheng Chu, Chia-Ping Chen and Chu-Song Chen
Asian Conference on Computer Vision (ACCV) 2010, Queenstown, New Zealand.
[ pdf ] [ presentation ] [ supplementary (15,355KB) ] [ bibtex ]
Copyright- These datasets are collected from Flickr and Google image search without permission from the original copyright holders. By downloading these files, you agree not to hold the authors or Academia Sinica liable for any damage, lawsuits, or other loss resulting from the possession or use of files. If you are the copyright owner of one of these images and would like it removed from the dataset, please contact Wen-Sheng Chu.
Release Notes- [Nov 12, 2010] Paper, presentation and supplementary online.
- [Sep 24, 2010] First release of dataset and groundrturh.
Feedback- Wen-Sheng Chu (wensheng.chu[at]gmail.com)